SavedAdd your journal

Journals / Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics / 2019 / Cilt: 68 - Sayı: 1

Determining the most vital arcs on the shortest path for fire trucks in terrorist actions that will cause fire

Ertuğrul AYYILDIZ
Gökhan ÖZÇELİK
Cevriye TEMEL GENCER
Pages
441–450
Publication Date
2019-02-01
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result, the bi-level model of the problem is presented first, and the model is applied on a numerical explanatory example.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In case of fire, the supplying of the water requirements of the fire area is a vital issue.
📊
Key Findings & Data:The water requirements must be satisfied as quickly as possible without encountering any obstacles.
💡
Conclusion & Impact:As a result, the bi-level model of the problem is presented first, and the model is applied on a numerical explanatory example.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Shortest path · network interdiction · mathematical modeling

Abstract

In case of fire, the supplying of the water requirements of the fire area is a vital issue. The water requirements must be satisfied as quickly as possible without encountering any obstacles. In the study, once a terrorist attack which will cause fire at certain area (node) is occurred, the situation in which the terrorists want to prevent the fire trucks' transportation to this area via the shortest path is considered. The main logic of the study is determining the risky arc(s) that will interdict and presenting a relatively safety paths for the fire trucks. Terrorists wants to maximize the shortest path of fire trucks depending on limited interdiction budget. In this context, the problem is considered within the framework of the Network Interdiction Problem (NIP), where there are two opposite sides as leader (terrorist) and follower (fire truck). As a result, the bi-level model of the problem is presented first, and the model is applied on a numerical explanatory example.

🔖

Cite This Article

AYYILDIZ, E., ÖZÇELİK, G., GENCER, C. T. (2019). Determining the most vital arcs on the shortest path for fire trucks in terrorist actions that will cause fire. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, Cilt: 68 - Sayı: 1, 441–450. https://ajindex.com/articles/747057

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (17)

  1. Israeli, E. and Wood, R.K., Shortest- path network interdiction. Networks, 40(2), (2002), 97-111.
  2. Wollmer, R.D., Some methods for determining the most vital link in a railway network. RM-3321-ISA, The Rand Corporation, Santa Monica, California, 1963.
  3. Lubore, S.H. and Scilia, G.T., Determining the most vital link in a flow network, DTIC Document, 1971.
  4. Wollmer, R., Removing arcs from a network. Operations Research, 12(6), (1964), 934-940.
  5. Ratliff, H.D., Sicilia, G.T. and Lubore, S.H., Finding the n most vital links in flow networks. Management Science, 21(5), (1975), 531-539.
  6. Malik, K., Mittal, A.K. and Gupta, S.K., The k most vital arcs in the shortest path problem. Operations Research Letters, 8(4), (1989), 223-227.
  7. Ball, M.O., Golden, B.L. and Vohra, R. V., Finding the most vital arcs in a network. Operations Research Letters, 8(2), (1989), 73-76.
  8. Lin, K.-C. and Chern, M.-S., The fuzzy shortest path problem and its most vital arcs. Fuzzy Sets and Systems, 58(3), (1993), 343-353.
  9. Jiang, Y. and Hu, A., Finding the Most Vital Link with Respect to the Characteristic of Network Communication. Journal of Networks, (2011) 6(3). Wood, R.K., Deterministic network interdiction. Mathematical and Computer Modelling, 17(2), (1993), 1-18.
  10. Cormican, K.J., Morton, D.P., Stochastic network interdiction, Operations Research, 46(2), (1998), 184--197. Brown, G. et al., Defending critical infrastructure. Interfaces, 36(6), (2006), 530-544.
  11. Brown, G.G. et al., Interdicting a nuclear-weapons project. Operations Research, 57(4), (2009), 866-877.
  12. Lim, C. and Smith, J.C., Algorithms for discrete and continuous multicommodity flow network interdiction problems. IIE Transactions, 39(1), (2007), 15-26.
  13. Salmeron, J., Wood, K. and Baldick, R., Worst-case interdiction analysis of large-scale electric power grids. IEEE Transactions on power systems, 24(1), (2009), 96-104.
  14. Akgün, A., Tansel, B.A. and Wood, R.K., The multi-terminal maximum-flow network- interdiction problem. European Journal of Operational Research, 211(2), (2011), 241-251.
  15. Kennedy, K.T. et al., Nodal interdiction. Mathematical and Computer Modelling, 54(11), (2011), 3116-3125.
  16. Malaviya, A., Rainwater, C. and Sharkey, T., Multi-period network interdiction problems with applications to city-level drug enforcement. IIE Transactions, 44(5), (2012), 368-380.
  17. Granata, D., Steeger, G. and Rebennack, S., Network interdiction via a critical disruption path: branch-and-price algorithms. Computers and Operations Research, 40(11), (